图形节点分类用于预测基因中的自闭症风险
Danushka Bandara1, Kyle Riccardi1
1Department of Computer Science and Engineering, Fairfield University, Fairfield, CT 06824, USA.
Genes
|April 27, 2024
概括
图形神经网络通过分析基因网络有效地识别自闭症谱系障碍 (ASD) 的遗传风险. Graph Sage模型在分类与自闭症相关的基因方面表现出卓越的表现.
科学领域:
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 自闭症谱系障碍 (ASD) 具有重要的遗传成分,但确定特定的风险基因仍然具有挑战性.
- 了解基因相互作用和染色体位置对于确定对ASD的遗传贡献至关重要.
- 图形神经网络 (GNN) 为分析复杂的生物网络提供了一个强大的框架.
研究的目的:
- 使用图形神经网络 (GNN) 探索与自闭症谱系障碍 (ASD) 的遗传风险关联.
- 开发和评估GNN模型来对基因的自闭症风险进行分类.
- 评估染色体带位置和蛋白质相互作用对ASD风险分类的贡献.
主要方法:
- 使用Sfari数据集和蛋白质相互作用网络 (PIN) 数据构建了一个基因网络,基因作为节点,相互作用作为边缘.
- 采用了GNN架构,包括图形卷积网络,Graph Sage和图形变压器,以及多层感知器基线.
- 执行了三个分类任务:二进制风险关联,多类风险关联和综合征基因关联.
主要成果:
- 在所有分类任务中,Graph Sage模型始终优于其他架构.
- 废除研究证实了染色体带位置和蛋白质相互作用在模型性能中的重要性.
- 实现了高准确度:85.80%的二进制风险,81.68%的多类风险,90.22%的综合症分类.
结论:
- 基因基因组 (GNN),特别是图形,是分类与ASD相关基因的有效工具.
- 整合基因相互作用和染色体位置数据可以提高ASD遗传风险预测的准确性.
- 这种方法有望促进对ASD遗传因素的理解和鉴定.
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